Summarising your data

GVPT399F: Power, Politics, and Data

Create summaries with `summarise()`

summarise(
  gapminder, avg_pop = mean(pop), avg_gdp_per_cap = mean(gdpPercap)
)

# A tibble: 1 × 2
    avg_pop avg_gdp_per_cap
      <dbl>           <dbl>
1 29601212.           7215.

Create summaries with `summarise()`

summarise(
  gapminder, 
  avg_pop = mean(pop), 
  median_pop = median(pop), 
  avg_gdp_per_cap = mean(gdpPercap),
  median_gdp_per_cap = median(gdpPercap)
)

# A tibble: 1 × 4
    avg_pop median_pop avg_gdp_per_cap median_gdp_per_cap
      <dbl>      <dbl>           <dbl>              <dbl>
1 29601212.   7023596.           7215.              3532.

Creating grouped summaries with `group_by()` and `summarise()`

gapminder_continent <- group_by(gapminder, continent)

summarise(
  gapminder_continent, 
  avg_pop = mean(pop), 
  avg_gdp_per_cap = mean(gdpPercap)
)

# A tibble: 5 × 3
  continent   avg_pop avg_gdp_per_cap
  <fct>         <dbl>           <dbl>
1 Africa     9916003.           2194.
2 Americas  24504795.           7136.
3 Asia      77038722.           7902.
4 Europe    17169765.          14469.
5 Oceania    8874672.          18622.